COVID-19 - Confirmed Cases vs. Deaths
Analysis comparing the number of confirmed cases to the number of deaths across countries.
# # create diffs
# df_cases['new_deaths'] = df_cases.deaths.diff()
# # flatten artifacts from one country to the next
# df_cases[df_cases['new_deaths']<0] = 0
# df_cases['new_confirmed'] = df_cases.confirmed.diff()
# df_cases[df_cases['new_confirmed']<0] = 0
#hide
# create diffs
df_cases['new_deaths'] = df_cases.deaths.diff()
# flatten artifacts from one country to the next
df_cases.loc[df_cases['new_deaths']<0, 'new_deaths'] = 0
df_cases['new_confirmed'] = df_cases.confirmed.diff()
df_cases.loc[df_cases['new_confirmed']<0, 'new_confirmed'] = 0
Estimated time of infection vs. confirmed infections
Following the ideas from Tomas Pueyo's Medium post "Coronavirus: Why You Must Act Now" [1], we assume the average time from infection to death at 23 days [2]. The data is pulled from the COVID-19 Data Repository by Johns Hopkins CSSE [3] every hour. Countries with a minimnum of 1000 confirmed cases and 40 confirmed deaths are included in this analysis.
The time from infection to death is equal to the incubation period plus the time from symptoms to death. This is used to estimate the time of the infections that lead to the observed deaths. We take the last fatality rate per country (total_cases/total_deaths) to estimate the number of infections that are responsible for the observed deaths.
In the figures below, you can observe successive waves of infections (dashed), detections (black) and deaths (red) for each country. The upper panel shows the absolute number of events. The dashed lines show the estimated number of infections. The lower panel shows the normalized number of events. Here the temporal delay between the waves and the relative change between each other can be observed.
[1] https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca
[2] https://github.com/midas-network/COVID-19/tree/master/parameter_estimates/2019_novel_coronavirus
[3] https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
df_cases_country = dff2[dff2['country']=="Germany"]
df_cases_country
Ahead of the curve
Some countries start testing the population earlier in the outbreak than others. The time delay between the wave of deaths and the wave of confirmed cases is indicative for how early a country is detecting new cases ahead of the increase of deaths. Earlier detection means a better chances for successful isolation of an infected person and treatment of the desease.
We measure the distance of the maximum of cumulative deaths and new deaths to the number of infections to estimate the progression of the infection across countries.
If, in the early phase of the infection wave, the number of deaths rises faster than the number of confirmed cases, the distance drops, indicating that
A comparison of countries with respect to their mean time for reponse is presented below.
To determine the above values, we plot the number of confirmed cases (solid black lines) and the number of deaths (dashed black lines). From this, we measure the distance of the day of maximum deaths (dashed red lines) to the day of confirmed cases at this y-value.
The distance is indicative for how fast the humber of confirmed cases increases comapred to the increase of the number of deaths.
